Dependable Parallel Multi-population Improved Brain Storm Optimization with Differential Evolution for On-line Energy Plant Optimal Operation Planning

This paper proposes on-line energy plant optimal operation planning (EPOOP) using dependable parallel multi-population improved brain storm optimization with differential evolution (DPMP-IBSODE). Parallel distributed processing (PDP) enables on-line optimal operation planning for a large number of energy plants with a few cluster computers, and therefore reduces the operational cost. While speedy EPOOP has been studied using PDP, there are still challenges for countermeasures for malfunction of computer processes such as breakdown or response delay of cluster computers and communication delay of local area networks (LANs). In order to execute on-line calculation of EPOOP with appropriate solution quality, dependable parallel EPOOP is required, where operational set points for various energy facilities are continuously sent to distributed control systems (DCSs). By formulating On-line EPOOP as a mixed integer nonlinear optimization programming (MINLP) problem, in this paper, the proposed method has been verified to be able to keep higher quality solutions at high speed than conventional method.